Deep learning--based models have surpassed classical machine learning--based
approaches in various text classification tasks, including sentiment analysis, news …

Recurrent neural networks (RNNs) have been widely adopted in research areas concerned
with sequential data, such as text, audio, and video. However, RNNs consisting of sigma …

Sentiment analysis has been a well-studied research direction in computational linguistics.
Deep neural network models, including convolutional neural networks (CNN) and recurrent …

The explosion of social media allowed individuals to spread information without cost, with
little investigation and fewer filters than before. This amplified the old problem of fake news …

The rapid increase in human population and development in technology have sharply
raised power consumption in today's world. Since electricity is consumed simultaneously as …

In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …

Neural network models have been widely used in the field of natural language processing
(NLP). Recurrent neural networks (RNNs), which have the ability to process sequences of …

Research on machine assisted text analysis follows the rapid development of digital media,
and sentiment analysis is among the prevalent applications. Traditional sentiment analysis …

Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has
become an inseparable part of our daily lives. It is considered as a convenient platform for …

Analyzing human multimodal language is an emerging area of research in NLP. Intrinsically
this language is multimodal (heterogeneous), sequential and asynchronous; it consists of …